CN115134407B - Active region determination method, device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses an active region determining method, an active region determining device, computer equipment and a storage medium, wherein the active region determining method comprises the following steps: acquiring positioning data reported by an application program and acquiring time of the positioning data; determining the time period of the positioning data according to the acquisition time; analyzing the positioning data to obtain the area of the user; classifying the positioning data into preset user position groups based on the region and the belonging time period; and inputting the user position groups into a preset model to obtain an active region of the user. And the active region of the user is output through the preset model, so that the accuracy of positioning the geographical region where the user is improved. Meanwhile, the obtained active region of the user effectively reserves the information quantity of the positioning data, and further the active positions of the user in different regions and different time periods can be effectively reflected.
Description
Technical Field
The present invention relates to the field of positioning, and in particular, to a method and apparatus for determining an active area, a computer device, and a storage medium.
Background
The geographical area of the user is an important user basic attribute, and is a measurement index with important value in the business wind control scene. The abnormal situation can be monitored through the geographical area of the user, and specifically, whether the user is in or out of the jurisdiction, whether a resident city exists, whether the transaction service address coincides with a specific area, whether the reported address is true and valid and the like can be judged through the geographical area of the user.
In the current business wind control scenario, the geographical area of the user is usually obtained according to the IP (Internet Protocol ) address of the user. The accuracy of the address location information cannot be effectively guaranteed by locating the geographical area of the user through the IP address, and meanwhile, the accuracy of the address location information can only reach the city level. In the current marketing scenario, the geographical area in which the user is located is defined by the address filled in by the user. However, the defined geographical area also suffers from poor accuracy and aging.
Disclosure of Invention
In view of the above, the present invention aims to overcome the shortcomings in the prior art, and provide an active region determining method, an active region determining device, a computer device and a storage medium, so as to solve the problem of poor accuracy of the geographic region where the located user is located.
In a first aspect, the present application provides a method for determining an active region, the method comprising:
Acquiring positioning data reported by an application program and acquiring time of the positioning data;
determining the time period of the positioning data according to the acquisition time;
analyzing the positioning data to obtain the area of the user;
classifying the positioning data into preset user position groups based on the region and the belonging time period;
And inputting the user position group into a preset model to obtain an active region of the user, wherein the region comprises the active region.
With reference to the first aspect, in a first possible implementation manner, the inputting the user location group into a preset model to obtain an active area of the user includes:
Inputting the user position group into a preset model to obtain a graph set comprising a first number of preset graphs;
and obtaining the active region of the user based on the weight of each preset graph in the graph set.
With reference to the first aspect, in a second possible implementation manner, the analyzing the positioning data to obtain an area where the user is located includes:
converting the positioning data into a longitude and latitude coordinate system in a preset format;
And calculating the hash value of the longitude and latitude coordinate system, and inquiring the area corresponding to the hash value based on a preset hash value table.
With reference to the first aspect, in a third possible implementation manner, the preset model is a gaussian mixture model.
With reference to the first aspect, in a fourth possible implementation manner, the classifying the positioning data into a preset user location group based on the location area and the belonging time period includes:
classifying the positioning data to a preset user position group based on the region, the belonged time period and the acquired identification information of the user.
With reference to the first aspect, in a fifth possible implementation manner, after classifying the positioning data into a preset user location group based on the location area and the belonging time period, the method further includes:
Judging whether the quantity of the positioning data included in the user position group exceeds a first threshold value;
If the number of the positioning data included in the user position group exceeds a first threshold, filtering abnormal positioning data, and then executing the step of inputting the user position group into a preset model to obtain an active region of a user;
and if the number of the positioning data included in the user position group does not exceed a first threshold, inputting the user position group into a preset model to obtain an active region of the user.
With reference to the first aspect, in a sixth possible implementation manner, after the inputting the user location group into a preset model, obtaining an active area of the user, the method further includes:
Acquiring an address location scattered point to be verified, and judging whether the address location scattered point to be verified is in the active area or not;
and if the address location scattered point to be verified is not in the active area, marking the address location scattered point to be verified as an abnormal location scattered point.
In a second aspect, the present application provides an active region determining apparatus, the apparatus comprising:
the positioning data acquisition module is used for acquiring positioning data reported by an application program and acquisition time of the positioning data;
the time period determining module is used for determining the time period of the positioning data according to the acquisition time;
The data analysis module is used for analyzing the positioning data to obtain the area where the user is located;
The data classifying module is used for classifying the positioning data to a preset user position group based on the region and the belonging time period;
and the active region obtaining module is used for inputting the user position group into a preset model to obtain an active region of the user, wherein the region comprises the active region.
In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, implements the active region determination method according to the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the active region determination method according to the first aspect.
The application provides an active region determining method, which comprises the following steps: acquiring positioning data reported by an application program and acquiring time of the positioning data; determining the time period of the positioning data according to the acquisition time; analyzing the positioning data to obtain the area of the user; classifying the positioning data into preset user position groups based on the region and the belonging time period; and inputting the user position groups into a preset model to obtain an active region of the user. And the active region of the user is output through the preset model, so that the accuracy of positioning the geographical region where the user is improved. Meanwhile, the obtained active region of the user effectively reserves the information quantity of the positioning data, and further the active positions of the user in different regions and different time periods can be effectively reflected.
Drawings
In order to more clearly illustrate the technical solutions of the present invention, the drawings that are required for the embodiments will be briefly described, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope of the present invention. Like elements are numbered alike in the various figures.
Fig. 1 shows a flowchart of an active region determining method according to an embodiment of the present invention;
FIG. 2 illustrates an exemplary diagram of a first active region provided by an embodiment of the present invention;
FIG. 3 illustrates an exemplary diagram of a second active region provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an active region determining apparatus according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present invention.
The terms "comprises," "comprising," "including," or any other variation thereof, are intended to cover a specific feature, number, step, operation, element, component, or combination of the foregoing, which may be used in various embodiments of the present invention, and are not intended to first exclude the presence of or increase the likelihood of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the invention belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having a meaning that is the same as the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in connection with the various embodiments of the invention.
Example 1
Referring to fig. 1, fig. 1 shows a flowchart of an active region determining method according to an embodiment of the present invention. The active region determination method in fig. 1 includes the steps of:
Step 110, acquiring positioning data reported by an application program and acquisition time of the positioning data.
An Application (App) collects a current position of a user in real time during a running process, wherein the positioning data may be data used for representing the real-time position of the user, such as GPS (Global Positioning System) data, which is not limited herein. The acquisition time is the time for which the application program acquires the user's position in real time. And acquiring positioning data reported by an application program and acquisition time of the positioning data. The positioning data is a measurement index with important value in the business wind control scene, and can effectively reflect whether the position information of the user is abnormal or not. It should be appreciated that positioning data reported by a plurality of heterogeneous applications may be acquired simultaneously to provide authenticity of the positioning data. After the positioning data are obtained, the abnormal positioning data are filtered, which is not described herein.
Step 120, determining the time period of the positioning data according to the acquisition time.
The preset division of the multiple time periods may specifically divide the working day time period and the weekend time period, may divide the day time period and the night time period, and may divide the day average into multiple time periods. Each time period is set according to actual requirements, and is not limited herein. And determining the time period of the positioning data according to the acquisition time of the positioning data. Specifically, it is assumed that the weekday period and the weekend period are divided in advance. And when the acquisition time of the positioning data is one week, determining that the time period of the positioning data is a working day time period. And when the acquisition time of the positioning data is Saturday, determining that the time period of the positioning data is a weekend time period. The positioning data may be categorized into different groupings by the time period to which the positioning data belongs.
And 130, analyzing the positioning data to obtain the area of the user.
The positioning data reported by the application program is a specific scattered point of the user position, and the positioning data reported by the application program is analyzed to obtain the area of the user. Specifically, when the analyzed positioning data determines that the user position is in the area a, the area where the user is located is determined to be the area a. The positioning data may be categorized into different groupings by the area in which the user is located.
It should be understood that the range accuracy of the area may be at the city level or the county level, and the range accuracy of the area is set according to the actual requirement, which is not limited herein.
As an example, the analyzing the positioning data to obtain the area where the user is located includes:
converting the positioning data into a longitude and latitude coordinate system in a preset format;
And calculating the hash value of the longitude and latitude coordinate system, and inquiring the area corresponding to the hash value based on a preset hash value table.
To improve the accuracy of locating the user's location, it is often necessary to parse a large amount of location data. For example, 7 hundred million pieces of positioning data reported by various application programs are acquired, and the positioning data is analyzed by using an LBS (Location Based Services based on mobile location service) interface, which takes more than 3 months, resulting in poor timeliness of analyzing the obtained data.
In this embodiment, a hash algorithm is used to analyze the positioning data to obtain the region where the user is located. Specifically, the geographic environment of the geography is recursively decomposed into a plurality of sub-blocks based on the longitude and latitude, and the longitude and latitude within the range of each sub-block has a unique coded ID (Identity document, identification number). And converting the positioning data reflecting the position scattered points of the user into an index structure to carry out matching inquiry so as to obtain the area of the user. And the hash algorithm is adopted to analyze the application program to report the positioning data, the time for analyzing the data is shortened to 20 minutes, and the timeliness of analyzing the obtained data is obviously improved.
TABLE 1
Referring to table 1, table 1 shows a preset hash value table provided in an embodiment of the present invention. And converting the positioning data reported by various application programs in different formats into a longitude and latitude coordinate system in a preset format. It should be understood that the preset format is selected according to actual requirements, and is not limited herein. In this embodiment, the latitude and longitude coordinate system of the preset format is a golde coordinate system. And calculating a hash value of the longitude and latitude coordinate system, and inquiring the area corresponding to the hash value based on a preset hash value table.
For example, assume that the latitude and longitude are 114.212288 and 30.624025, and the hash value is 8840a680b9fffff. Based on a preset hash value table, inquiring the region corresponding to the hash value to obtain that the region of the user is the mouth region of Wuhan City in Hubei province. Taking Jilin province general city light Nan County as an example, when a preset hash value table is constructed, each region is recursively decomposed into a plurality of sub-blocks, and the longitude and latitude within the range of each sub-block have unique coding IDs. When calculating hash values of the longitude and latitude coordinate system, all the areas corresponding to the hash values are Jilin province general electric Nan County, and table 1 does not specifically show all the hash values and the areas.
And step 140, classifying the positioning data into preset user position groups based on the region and the belonging time period.
Pre-building a preset user position group corresponding to each located area and each time period, for example, pre-building a user position group of a first time period of an area A, a user position group of a second time period of the area A, a user position group of a first time period of an area B and a user position group of a second time period of the area B. Classifying the positioning data into preset user position groups based on the region where each positioning data is located and the time period to which each positioning data belongs. Each preset user position group furthest reserves the original information quantity of positioning data and reflects the information such as the position scattered point density of a user in a specific time period of a specific area.
As an example, the classifying the positioning data into a preset user location group based on the location area and the belonging time period includes:
classifying the positioning data to a preset user position group based on the region, the belonged time period and the acquired identification information of the user.
Because the application program can report the positioning data of different users, the positioning data are classified into preset user position groups based on the area, the belonged time period and the acquired identification information of the users. Providing a plurality of preset user position groups corresponding to each user, and obtaining the position information of the user in different areas and different time periods through positioning data in the preset user position groups.
It should be understood that the identification information of the user may be any information for identifying the identity of the user, such as a name, a evidence number, a mobile terminal number, and the like, which is not limited herein.
And step 150, inputting the user position group into a preset model to obtain an active region of the user.
And inputting the user position group as an input variable into a preset model to obtain an output variable output by the preset model, and obtaining the active region of the user based on the structured output variable. Because each input user position packet corresponds to the position information of the user in the time period of the area where the user is located, the output result of the preset model effectively reserves the information quantity of the original positioning data and effectively reflects the active position of the user in the time period of the user and the area where the user is located. It is to be understood that the active area is an area with a high density of user location discontinuities. The located area includes an active area, that is, the active area is one of the areas where the users are located, and each of the areas where the users are located may include a plurality of active areas, which are not described herein.
Referring to fig. 2 together, fig. 2 is a schematic diagram illustrating a first active region according to an embodiment of the present invention. As an example, the inputting the user location group into a preset model to obtain an active area of the user includes:
Inputting the user position group into a preset model to obtain a graph set comprising a first number of preset graphs;
and obtaining the active region of the user based on the weight of each preset graph in the graph set.
In this embodiment, the preset pattern is an elliptical pattern. And inputting the user position group comprising the positioning data into a preset model to obtain a graphic set comprising a first number of elliptical graphics output by the preset model, wherein the first number is changed according to the preset model output, and the method is not limited herein. The positioning data represents the position scattered points of the user, and the weight of each elliptic graph is obtained according to the density of the position scattered points of the user and the density center point, the long axis, the short axis and the anticlockwise rotation included angle of each elliptic graph. And obtaining the active region of the user in the region and the belonging time period based on the weight of each elliptic graph in the graph set. As shown, the preset model outputs an image set comprising a first number of elliptical patterns, which can be equivalent to contours in the topography map. From the image set, two active regions of the user in the region and the belonging time period are determined.
As an example, the preset model is a gaussian mixture model.
Gaussian mixture models (Gaussian Mixture Model, GMM) typically output a set of images that are circular or elliptical. Compared with other clustering models such as a KNN (K-NearestNeighbor, adjacent algorithm) model, a Kmeans (K-means clustering algorithm ) model, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) model and the like, the output result of the Gaussian mixture model keeps more information, the active region of the user can be obtained to accurately reflect the activity rule of the user, and the method is applicable to more scenes. Compared with an irregular polygonal result, the elliptical graph of the output of the Gaussian mixture model reduces the storage cost and the use cost of data. Compared with an irregular polygon, the regular elliptic graph reduces the calculation complexity when calculating whether a point is in the polygon and the geographical geometric problems such as region overlapping and the like.
Referring to fig. 3 together, fig. 3 is a schematic diagram illustrating a second active region according to an embodiment of the present invention. The confidence interval of the active region of the user can be set, wherein the range of the confidence interval is set according to the actual requirement, and the confidence interval is not limited herein. In this embodiment, three confidence intervals of 90%, 95% and 99% are set, respectively. The confidence interval of the active region of the user is set, and the obtained effective grade of the active region of the user is adjusted, so that the obtained active region can further reflect the activity rule of the user, and the method is further applicable to more scenes and meets the requirement of diversity.
As an example, after classifying the positioning data into a preset user location group based on the location area and the belonging time period, the method further includes:
Judging whether the quantity of the positioning data included in the user position group exceeds a first threshold value;
if the number of the positioning data included in the user position group exceeds a first threshold, step 150 is executed after abnormal positioning data are filtered, and the user position group is input into a preset model to obtain an active region of a user;
If the number of the positioning data included in the user location group does not exceed the first threshold, step 150 is executed, where the user location group is input to a preset model, so as to obtain an active area of the user.
If the active area of the user exists in the area where the user is located, a large amount of positioning data reported by the application program is acquired in the period time. If the number of the positioning data included in the user position group does not exceed the first threshold, inputting the user position group into a preset model to obtain an active area of the user, wherein the first threshold is set according to actual requirements and is not limited herein. If the number of the positioning data included in the user position group exceeds a first threshold, abnormal positioning data is filtered according to the acquisition time of the positioning time and the time of reporting the positioning data by the application program, so that the accuracy of the obtained active region of the user is improved. And inputting the user position groups with abnormal positioning data filtered to a preset model to obtain an active region of the user.
It is to be understood that the second threshold value may also be set. If the number of the positioning data included in the user position group does not exceed the second threshold, determining that the number of the positioning data in the user position group is insufficient, reserving the positioning data in the user position group, and not inputting the user position group into the preset model. In addition, the user positions of which the number of the positioning data does not exceed the second threshold value can be grouped and marked as abnormal groups, and whether the user information is broken through, stolen and other abnormal conditions can be determined. The first threshold and the second threshold are set according to actual requirements, and are not limited herein. In this embodiment, the first threshold is 180, and the second threshold is 2.
As an example, after the grouping of the user positions is input to a preset model to obtain the active area of the user, the method further includes:
Acquiring an address location scattered point to be verified, and judging whether the address location scattered point to be verified is in the active area or not;
and if the address location scattered point to be verified is not in the active area, marking the address location scattered point to be verified as an abnormal location scattered point.
After the active area of the user is obtained, if the address location scattered points to be verified, which are filled in by the user, are obtained, whether the address location scattered points to be verified are in the active area is judged. And if the address location scattered point to be verified is not in the active area, marking the address location scattered point to be verified as a trusted location scattered point. If the address location scattered points to be verified are not in the active area, marking the address location scattered points to be verified as abnormal location scattered points, and calculating the credibility of the abnormal location scattered points.
It should be understood that whether the address location scattered points to be verified filled in by the user are real and credible can be judged through the user active area, and the obtained user active locations can be applied to other scenes, such as the scenes that the user is in or out of the jurisdiction, whether a resident city exists, whether the transaction service address coincides with a specific area, and the like, which are not described herein.
The application provides an active region determining method, which comprises the following steps: acquiring positioning data reported by an application program and acquiring time of the positioning data; determining the time period of the positioning data according to the acquisition time; analyzing the positioning data to obtain the area of the user; classifying the positioning data into preset user position groups based on the region and the belonging time period; and inputting the user position groups into a preset model to obtain an active region of the user. And the active region of the user is output through the preset model, so that the accuracy of positioning the geographical region where the user is improved. Meanwhile, the obtained active region of the user effectively reserves the information quantity of the positioning data, and further the active positions of the user in different regions and different time periods can be effectively reflected.
Example 2
Referring to fig. 4, fig. 4 is a schematic structural diagram of an active region determining apparatus according to an embodiment of the present invention. The active region determination apparatus 200 in fig. 4 includes:
The positioning data obtaining module 210 is configured to obtain positioning data reported by an application program and a collection time of the positioning data;
A time period determining module 220, configured to determine, according to the acquisition time, a time period to which the positioning data belongs;
The data analysis module 230 is configured to analyze the positioning data to obtain an area where the user is located;
a data classifying module 240, configured to classify the positioning data into a preset user location group based on the location area and the belonging time period;
And the active region obtaining module 250 is configured to input the user location group to a preset model to obtain an active region of the user, where the located region includes the active region.
As an example, the active region obtaining module 250 includes:
The graphic set obtaining submodule is used for inputting the user positions into a preset model in a grouping mode to obtain a graphic set comprising a first number of preset graphics;
And the active region sub-module is used for obtaining the active region of the user based on the weight of each preset graph in the graph set.
As one example, the data parsing module 230 includes:
the longitude and latitude sub-module is used for converting the positioning data into a longitude and latitude coordinate system with a preset format;
the region determination submodule is used for calculating the hash value of the longitude and latitude coordinate system and inquiring the region corresponding to the hash value based on a preset hash value table.
As an example, the preset model is a gaussian mixture model.
As an example, the data classifying module 240 is further configured to classify the positioning data into a preset user location group based on the location area, the belonging time period, and the obtained identification information of the user.
As an example, the active region determining apparatus 200 further includes:
A number judging module, configured to judge whether the number of positioning data included in the user location packet exceeds a first threshold;
the active region obtaining module 250 is further configured to, if the number of positioning data included in the user location group exceeds a first threshold, perform a step of inputting the user location group into a preset model to obtain an active region of the user after filtering abnormal positioning data;
The active region obtaining module 250 is further configured to perform the step of inputting the user location group into a preset model to obtain an active region of the user if the number of positioning data included in the user location group does not exceed a first threshold.
As an example, the active region determining apparatus 200 further includes: acquiring an address location scattered point to be verified, and judging whether the address location scattered point to be verified is in the active area or not;
and the abnormal position marking module is used for marking the address position scattered points to be verified as abnormal position scattered points if the address position scattered points to be verified are not in the active area.
The active region determining apparatus 200 is configured to perform the corresponding steps in the above-described active region determining method, and specific implementation of each function is not described herein. In addition, the alternative example in embodiment 1 is also applicable to the active region determination apparatus 200 of embodiment 2.
The embodiment of the present application further provides a computer device, where the computer device further includes a processor and a memory, where the memory stores a program or instructions that, when executed by the processor, implement the steps of the active area determining method according to embodiment 1.
The embodiment of the present application also provides a computer-readable storage medium having stored thereon a program or instructions which, when executed by a processor, implement the steps of the active region determination method described in embodiment 1.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flow diagrams and block diagrams in the figures, which illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules or units in various embodiments of the invention may be integrated together to form a single part, or the modules may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a smart phone, a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention.
Claims (9)
1. A method of active region determination, the method comprising:
Acquiring positioning data reported by an application program and acquiring time of the positioning data;
determining the time period of the positioning data according to the acquisition time;
converting the positioning data into a longitude and latitude coordinate system in a preset format;
Calculating a hash value of the longitude and latitude coordinate system, and inquiring a region corresponding to the hash value based on a preset hash value table;
recursively decomposing a geographic environment of a geography into a plurality of sub-blocks based on the longitude and latitude, wherein the longitude and latitude in the range of each sub-block have unique code IDs; converting the positioning data of the scattered points of the positions of the reaction users into an index structure to carry out matching inquiry so as to obtain the areas of the users;
classifying the positioning data into preset user position groups based on the region and the belonging time period;
And inputting the user position group into a preset model to obtain an active region of the user, wherein the region comprises the active region.
2. The method for determining an active region according to claim 1, wherein the step of inputting the user location group into a preset model to obtain the active region of the user includes:
Inputting the user position group into a preset model to obtain a graph set comprising a first number of preset graphs;
and obtaining the active region of the user based on the weight of each preset graph in the graph set.
3. The active region determination method according to claim 1, wherein the preset model is a gaussian mixture model.
4. The active region determination method of claim 1, wherein the categorizing the positioning data into a preset user location group based on the region and the time period comprises:
classifying the positioning data to a preset user position group based on the region, the belonged time period and the acquired identification information of the user.
5. The active region determination method of claim 1, wherein after classifying the positioning data into a preset user location group based on the region and the belonging period, further comprising:
Judging whether the quantity of the positioning data included in the user position group exceeds a first threshold value;
If the number of the positioning data included in the user position group exceeds a first threshold, filtering abnormal positioning data, and then executing the step of inputting the user position group into a preset model to obtain an active region of a user;
and if the number of the positioning data included in the user position group does not exceed a first threshold, executing the step of inputting the user position group into a preset model to obtain an active region of the user.
6. The method for determining an active region according to claim 1, wherein after the grouping of the user positions is input to a preset model to obtain the active region of the user, further comprising:
Acquiring an address location scattered point to be verified, and judging whether the address location scattered point to be verified is in the active area or not;
and if the address location scattered point to be verified is not in the active area, marking the address location scattered point to be verified as an abnormal location scattered point.
7. An active region determination apparatus, the apparatus comprising:
the positioning data acquisition module is used for acquiring positioning data reported by an application program and acquisition time of the positioning data;
the time period determining module is used for determining the time period of the positioning data according to the acquisition time;
the data analysis module is used for converting the positioning data into a longitude and latitude coordinate system in a preset format;
Calculating a hash value of the longitude and latitude coordinate system, and inquiring a region corresponding to the hash value based on a preset hash value table;
recursively decomposing a geographic environment of a geography into a plurality of sub-blocks based on the longitude and latitude, wherein the longitude and latitude in the range of each sub-block have unique code IDs; converting the positioning data of the scattered points of the positions of the reaction users into an index structure to carry out matching inquiry so as to obtain the areas of the users;
The data classifying module is used for classifying the positioning data to a preset user position group based on the region and the belonging time period;
and the active region obtaining module is used for inputting the user position group into a preset model to obtain an active region of the user, wherein the region comprises the active region.
8. A computer device, characterized in that it comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, implements the active area determination method according to any of claims 1 to 6.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the active area determination method according to any of claims 1 to 6.
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